Effect Size
A standardized measure of the magnitude of a difference or relationship, independent of sample size, such as a standardized mean difference or correlation.
Effect size quantifies how large an effect is, not merely whether it exists. Common examples include standardized mean differences for comparing groups, correlation coefficients for relationships, and variance-explained measures for models. Because effect sizes do not depend on sample size the way p-values do, they allow results to be compared across studies and are the raw material of meta-analysis.
Effect sizes are often labeled small, medium, or large by convention, but interpretation should also consider the field and the practical stakes of the outcome.
For a thesis, effect sizes turn "significant" into "how much." Report one for every main test, interpret it in context, and use an expected effect size when justifying your sample through a power analysis. Examiners frequently ask about practical significance, and effect sizes are the answer.
Writing the thesis this term belongs to?
CiteDash takes a thesis from research question to a compiled document, with AI that cites only real papers and verifies every claim against its source.